code: def calculate_validation_metric(
      pipeline: Pipeline, dataset_to_validate: InputData
  ) -> float:
      # the execution of the obtained composite models
      predicted = pipeline.predict(dataset_to_validate)
      # the quality assessment for the simulation results
      roc_auc_value = roc_auc(
          y_true=dataset_to_validate.target, y_score=predicted.predict
      )
      return roc_auc_value
  
file_path: FEDOT/cases/credit_scoring/credit_scoring_problem.py
imports: import logging
  from sklearn.metrics import roc_auc_score
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.utils import fedot_project_root
  from fedot.core.utils import set_random_seed
label_desc: Use image node_id 1 for calling a global function with example usage: calculate_validation_metric(pipeline, dataset_to_validate) and returns: roc_auc_value